Abstract:

In the evolving landscape of network security, traditional intrusion detection systems (IDS) often fall short in addressing the complexity and variety of modern cyber threats. This research introduces an advanced approach to network intrusion detection by developing an ensemble model that integrates multiple deep learning algorithms, specifically Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Autoencoders. The proposed ensemble model leverages the strengths of each algorithm to provide a more robust and accurate detection system. The study evaluates the performance of the ensemble model against standalone deep learning approaches, demonstrating significant improvements in accuracy, precision, recall, and F1-score. The ensemble model effectively reduces false positive and false negative rates, thus enhancing overall detection reliability. Additionally, the research explores various ensemble techniques, including stacking, bagging, and boosting, with stacking proving to be the most effective in optimizing performance. Optimization strategies such as hyperparameter tuning, dropout regularization, and early stopping were employed to further enhance the model’s efficiency. Despite the increased computational cost associated with the ensemble approach, the improved detection capabilities make it a valuable tool for real-time network intrusion detection. This research not only highlights the effectiveness of deep learning ensembles in cybersecurity but also provides a framework for integrating diverse algorithms to address the limitations of existing IDS approaches. The findings underscore the potential of ensemble deep learning models in advancing network security and offer a foundation for future exploration in this domain.

Description:

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